Abstract
Over the past two decades, several computational methods have been proposed to predict how missense mutations can affect protein structure and function, either by altering protein stability or interactions with its partners, shedding light into potential molecular mechanisms giving rise to different phenotypes. Effectively and efficiently predicting consequences of mutations on protein–nucleic acid interactions, however, remained until recently a great and unmet challenge. Here we report an updated webserver for mCSM–NA, the only scalable method we are aware of capable of quantitatively predicting the effects of mutations in protein coding regions on nucleic acid binding affinities. We have significantly enhanced the original method by including a pharmacophore modelling and information of nucleic acid properties into our graph-based signatures, considering the reverse mutation and by using a refined, more reliable data set, based on a new release of the ProNIT database, which has significantly improved the reliability and applicability of the methodology. Our new predictive model was capable of achieving a correlation coefficient of up to 0.70 on cross-validation and 0.68 on blind-tests, outperforming its previous version. The server is freely available via a user-friendly web interface at: http://structure.bioc.cam.ac.uk/mcsm_na.
Highlights
The interaction of proteins with DNA and RNA is essential for a wide variety of cellular processes, in particular for the proper regulation of gene expression, and DNA replication and repair
In order to assess the applicability of our mutational cutoff scanning matrices graph-based signatures in predicting the impact of mutations on protein–nucleic acid binding affinities, a dataset derived from ProNIT was used
The datasets were further classified based upon the nature of the nucleic acid - whether they included double-stranded DNA, singlestranded DNA or RNA (67 single-point mutations across five complexes)
Summary
The interaction of proteins with DNA and RNA is essential for a wide variety of cellular processes, in particular for the proper regulation of gene expression, and DNA replication and repair. We have previously used the concept of graph-based signatures to model a broad range of molecular phenomena This has included the effect of mutations on protein stability [17,18], and interactions with other proteins [18,19], small molecules [20,21,22] and metal ions [8]. By using a subset of high-quality data from the ProNIT database (version 2.0) [23], we have developed mCSM–NA using our graph-based signature concept, a method that provides a reliable, scalable way to predict and characterize the effect of a single point missense mutation on protein– nucleic acid binding.
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